Download CS 446-546 Syllabus v0 5a

CS 446/546 – Networks in Computational Biology
Catalog Description: This course is an introduction to biological networks and computational
methods for their analysis, inference, and functional modeling. Various network centralities,
topological measures, clustering algorithms, and probabilistic annotation models are introduced
in the context of protein interaction, gene regulatory, and metabolic networks. The course also
surveys bioinformatics methods for data-driven inference of network structure.
Credits: 3
Terms Offered: Fall
Prerequisites: CS 261 or equivalent. Recommended but not required: CS325 or equivalent.
Courses that require this as a prerequisite: None
Structure:
On campus:
Three 50-minute class sessions per week
Instructors: Stephen Ramsey
Course Content:
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Introduction to the class. Overview of syllabus. Discussion of class expectations. Set up computational
environment.
Introduction to biological networks
Graph theory fundamentals
Graph theory data structures
Degree & degree distribution; scale-free networks; attack tolerance
Clustering coefficients, density
Paths, geodesic paths, diameter, components, depth-first
Single-vertex shortest-paths
Distance-based centralities: closeness and eccentricity; cliques and cores
Betweenness centrality
Feedback-based centralities
Network similarity, matching index, topological overlap; Gene Ontology
Network community detection – global (Markov Clustering)
Network community detection – seed-and-extend
Assortative mixing, degree correlations
Transcriptional profilng; date hubs & party hubs
Subnetwork motifs; network statistical testing; gene regulatory networks
Correlation networks, weighted correlation network analysis
Partial correlation coefficients
Introduction to information theory; RELnet; CLR
Information theory II; ARACNe
Inference of protein-protein interactions – introduction to probability and Bayes's theorem
Inference of protein-protein interactions II – Naïve Bayes
Inference of probabilistic network structure from multivariate measurements with interventional data
Boolean networks - cell cycle
Maximum-flow, minimum-cuts
Metabolic network flow
Probabilistic prediction of protein function (MRF)
Learning Resources:
 MEJ Newman, Networks: an introduction. Oxford University Press, 2010.
 Dasgupta, Papadimitrou, and Vazirani (DPV). Algorithms. Free online PDF. 2006.
 Alpan Raval and Animesh Ray (RR). Introduction to biological networks. CRC Press, 2014.
 Björn Junker and Falk Schreiber (JS). Analysis of biological networks. Wiley 2008.
 Dehmer, Emmert-Streib, Graber, and Salvador (DEGS). Applied Statistics for Network
Biology. Wiley-Blackwell, 2011.
 Chen, Wang, and Zhang (CWZ). Biomolecular Networks. Wiley 2009.
In addition to the above textbooks, students will read research articles from the computational
biology literature that illustrate specific applications of the algorithms that we cover, to analyzing
or learning the structure of biological networks.
Measurable Student Learning Outcomes:
At the completion of the course, students will be able to…
1. demonstrate familiarity with different types of biological networks and their encodings as
graphs;
2. describe the limitations and performance characteristics for standard algorithms that are used
for analyzing biological networks and for data-driven inference of biological network structure.
3. describe, in writing and orally, the goal, methods and results of a computational investigation
of a biological network.
4. implement a computational workflow for inferring the structure of a biological network or
analyzing a defined biological network
Additional learning outcomes for CS546 students:
5. critically read and understand research articles that describe the use of computational network
analysis in life sciences applications;
6. design a computational workflow to analyze a given biological network using established
algorithms (see outcome 2 above) to answer a specific research question;
7. conduct an original research project in computational biology that is focused on a specific
hypothesis, incorporates appropriate analytical methods, and synthesizes findings into an overall
conclusion regarding the validity of the hypothesis
Evaluation of Student Learning: (see the course syllabus for grading rubric)
 homework (40% of final grade)
o two substantial homework assignments in which students will use various numerical
analysis and network analysis toolboxes (igraph, scikit-learn, SciPy, NumPy,
matplotlib, pandas, R, etc.) and biological datasets that the instructor will provide.
o Homework assignments will include a baseline set of problems that both CS446 and
CS546 students will be required to complete
o The two homework assignments for the CS546 students will also include algorithm
analysis problems that will require more formal and explicit mathematical reasoning
and proof construction
 in-class quizzes on the assigned reading material, one quiz per week (20% of final grade)
o CS546 students will be required to do additional readings in CLRS and DPV.
 in-class participation (20% of final grade)

o CS546 students will be required to demonstrate the ability to frame a mathematical
argument about the function or performance of an algorithm, at the whiteboard
in-class poster presentation on a final project (20% of final grade)
o students present the results of their individual projects at during the final class
session, and each student is required to create a brief written summary of the other
poster presentations and submit the summaries at the end of the class
o CS546 students will be required to serve as "TAs" for the poster session, in which
they will conduct a Q&A with the CS446 students about the CS446 students' posters
Statement regarding Students with Disabilities: Accommodations for students with
disabilities are determined and approved by Disability Access Services (DAS). If you, as a
student, believe you are eligible for accommodations but have not obtained approval please
contact DAS immediately at 541-737-4098 or at http://ds.oregonstate.edu. DAS notifies students
and faculty members of approved academic accommodations and coordinates implementation of
those accommodations. While not required, students and faculty members are encouraged to
discuss details of the implementation of individual accommodations.
Link to Statement of Expectations for Student Conduct, i.e., cheating policies
http://oregonstate.edu/studentconduct/offenses-0
Created: Winter 2015
Updated: Spring 2016
Updated: May 3, 2016